CN115130595B

CN115130595B - Prediction-based aircraft data analysis and maintenance system

Info

Publication number: CN115130595B
Application number: CN202210783954.9A
Authority: CN
Inventors: 廖灿星; 陈文佳; 黎强; 李想; 周杰; 郑跃贵; 汪元武
Original assignee: Chongqing College of Electronic Engineering
Current assignee: Chongqing College of Electronic Engineering
Priority date: 2022-07-05
Filing date: 2022-07-05
Publication date: 2023-06-27
Anticipated expiration: 2042-07-05
Also published as: CN115130595A

Abstract

The application provides an aircraft data analysis and maintenance system based on prediction relates to aircraft technical field, and this system includes: the acquisition module is used for acquiring historical flight data of the target aircraft; the prediction module is used for determining whether the aircraft has a fault according to the historical flight data and the fault prediction model, and if so, determining the fault type and the fault score of the aircraft, wherein the fault score is used for representing the severity degree of the fault type predicted by the fault prediction model; the fault degree determining module is used for determining the fault degree of the aircraft according to the fault scores and fault data in a fault database, wherein the fault data comprises at least one of fault occurrence frequency, fault weight, fault duration time and fault occurrence period; and the maintenance module is used for determining the maintenance strategy of the aircraft according to the fault degree and sending the maintenance strategy to the workbench.

Description

Prediction-based aircraft data analysis and maintenance system

Technical Field

The application relates to the technical field of aircrafts, in particular to an aircraft data analysis and maintenance system based on prediction.

Background

Aircraft belong to highly customized equipment, and technical maturity is generally not high compared with industrial products.

As the number and difficulty of task execution requirements for aircraft increases, data monitoring and analysis of aircraft has reached massive levels.

Therefore, if the flight data of the aircraft are monitored in real time, the flight data are missed, the analysis efficiency of the flight data is low, and the flight data after analysis cannot be combined to make timely and accurate maintenance and the like due to the large load of the flight data acquisition workload.

Disclosure of Invention

The embodiment of the invention aims to provide a prediction-based aircraft data analysis and maintenance system, which can remarkably improve the analysis efficiency of flight data of an aircraft and timely and accurately combine the analyzed data to maintain the aircraft with faults. The specific technical scheme is as follows:

in a first aspect of an embodiment of the present invention, there is provided a predictive-based aircraft data analysis and maintenance system, the system comprising:

the acquisition module is used for acquiring historical flight data of the target aircraft;

the prediction module is used for determining whether the aircraft has a fault according to the historical flight data and the fault prediction model, and if so, determining the fault type and the fault score of the aircraft, wherein the fault score is used for representing the severity degree of the fault type predicted by the fault prediction model;

the fault degree determining module is used for determining the fault degree of the aircraft according to the fault scores and fault data in a fault database, wherein the fault data comprises at least one of fault occurrence frequency, fault weight, fault duration time and fault occurrence period;

and the maintenance module is used for determining the maintenance strategy of the aircraft according to the fault degree and sending the maintenance strategy to the workbench.

Optionally, the prediction module is further specifically configured to:

determining a fault classification value corresponding to the historical data according to the historical flight data;

and determining the fault type according to the fault classification interval in which the fault classification value is positioned.

Optionally, the prediction module is further specifically configured to: and determining the fault score according to the fault type and the historical flight data corresponding to the fault type.

Optionally, the fault degree determining module is further specifically configured to:

obtaining the prediction accuracy of the fault prediction model;

determining a fault degree of the aircraft according to the prediction accuracy, the fault score and fault data in a fault database, wherein the fault degree can be expressed as:

；

wherein j is the failure mode,

for the prediction accuracy, S is the fault score, +.>

For the frequency of occurrence of said fault +.>

For the frequency of occurrence of said fault, +.>

For the fault weight, T is the fault duration, T is the fault occurrence period, +.>

And the failure degree of the failure mode j.

Optionally, the prediction module is further specifically configured to:

training an initial fault prediction model according to the historical flight data, and obtaining a trained fault prediction model when the training degree of the initial fault prediction model is greater than a preset threshold value;

the training degree is determined through the prediction capability of the initial fault prediction model on flight data with higher abnormality degree.

Optionally, the maintenance module is further specifically configured to:

acquiring a maintenance strategy model;

and determining the maintenance strategy and the processing type of the maintenance strategy according to the maintenance strategy model, the fault degree and the historical flight data corresponding to the fault degree.

Optionally, the acquiring module is further specifically configured to: and normalizing and de-normalizing the historical flight data.

In yet another aspect of an embodiment of the present invention, a method of prediction-based aircraft data analysis and repair is provided, the method comprising:

acquiring historical flight data of a target aircraft;

determining whether the aircraft has a fault according to the historical flight data and a fault prediction model, and if so, determining the fault type and fault score of the aircraft, wherein the fault score is used for representing the severity degree of the fault type predicted by the fault prediction model;

determining a fault degree of the aircraft according to the fault score and fault data in a fault database, wherein the fault data comprises at least one of fault occurrence frequency, fault influence probability, fault duration and fault occurrence period;

and determining a maintenance strategy of the aircraft according to the fault degree, and sending the maintenance strategy to a workbench.

In a further aspect of the embodiments of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which when executed implements the steps of the method as described above.

In yet another aspect of the embodiments of the present invention, a computer device is provided, comprising a processor, a memory and a computer program stored on the memory, the processor implementing the steps of the method as described above when executing the computer program.

The beneficial effects are that:

from the above, according to the embodiment of the application, whether the aircraft has faults, fault types, fault scores and the like can be effectively predicted through the historical flight data of the aircraft, the fault degree and the maintenance strategy of the aircraft can be provided after the data are obtained, and in addition, the real-time data of the aircraft can be analyzed, so that the fault data and the maintenance strategy can be effectively combined.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic illustration of an application scenario for a predictive-based aircraft data analysis and repair system provided in an embodiment of the present application;

FIG. 2 is a schematic structural view of a predictive-based aircraft data analysis and repair system provided in an embodiment of the present application;

FIG. 3 is a flow diagram of a predictive-based aircraft data analysis and maintenance system provided in an embodiment of the present application;

fig. 4 is an internal structural diagram of a computer device provided in an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described and illustrated below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden on the person of ordinary skill in the art based on the embodiments provided herein, are intended to be within the scope of the present application. Moreover, it should be appreciated that while such a development effort might be complex and lengthy, it would nevertheless be a routine undertaking of design, fabrication, or manufacture for those of ordinary skill having the benefit of this disclosure, and thus should not be construed as having the benefit of this disclosure.

Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by those of ordinary skill in the art that the embodiments described herein can be combined with other embodiments without conflict.

Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar terms herein do not denote a limitation of quantity, but rather denote the singular or plural. The terms "comprising," "including," "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to only those steps or elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means greater than or equal to two. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. The terms "first," "second," "third," and the like, as used herein, are merely distinguishing between similar objects and not representing a particular ordering of objects.

In this embodiment, a system for analyzing and maintaining data of an aircraft based on prediction is provided, and fig. 1 is a schematic diagram of an application scenario of the system for analyzing and maintaining data of an aircraft based on prediction provided in the embodiment of the present application, as shown in fig. 1, in the application environment, including an aircraft and a service end. The aircraft communicates with the server through a network; the service end acquires historical flight data of the aircraft, and gives out the fault degree and maintenance strategy of the aircraft by combining a fault prediction model and a maintenance strategy model of the service end. The aircraft may include, but is not limited to, balloons, airships, airplanes, satellites, manned spacecraft, space probes, space shuttles, etc., and the server may be implemented as a stand-alone server or as a cluster of servers.

In some embodiments, the network may be a wired network or a wireless network, or the like, or any combination thereof. By way of example only, the network may include a cable network, a wired network, a fiber optic network, a telecommunications network, an internal network, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a Bluetooth network, a zigbee network, a Near Field Communication (NFC) network, a global system for mobile communications (GSM) network, a Code Division Multiple Access (CDMA) network, a Time Division Multiple Access (TDMA) network, a General Packet Radio Service (GPRS) network, an enhanced data rates for GSM evolution (EDGE) network, a Wideband Code Division Multiple Access (WCDMA) network, a High Speed Downlink Packet Access (HSDPA) network, a Long Term Evolution (LTE) network, a User Datagram Protocol (UDP) network, a transmission control protocol/Internet protocol (TCP/IP) network, a Short Message Service (SMS) network, a Wireless Application Protocol (WAP) network, an Ultra Wideband (UWB) network, infrared, and the like, or any combination thereof. In some embodiments, the predictive-based aircraft data analysis and repair system 100 may include one or more network access points. For example, a base station and/or wireless access point, one or more components of the analysis and repair system may be connected to a network to exchange data and/or information based on predicted aircraft data.

It should be noted that the prediction-based aircraft data analysis and repair system is provided for illustrative purposes only and is not intended to limit the scope of the present application. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, the prediction-based aircraft data analysis and repair system may also include databases, information sources, and the like. As another example, the predictive-based aircraft data analysis and repair system may implement similar or different functions on other devices. However, such changes and modifications do not depart from the scope of the present application.

The embodiment of the application also provides a prediction-based aircraft data analysis and maintenance system, and fig. 2 shows a schematic structural diagram of the prediction-based aircraft data analysis and maintenance system provided in the embodiment of the application, where the system includes:

an acquisition module 201, configured to acquire historical flight data of the target aircraft.

The historical flight data refers to data generated by the aircraft in the historical flight task, and taking the unmanned aerial vehicle as an example, the historical flight data can include but is not limited to: attitude information, altitude information, speed information, rudder system status, voltage information, etc. of unmanned aerial vehicle flight. For example, the unmanned aerial vehicle may have one or more of a combined pitch angle, a combined roll angle, a combined yaw angle, a pitch angle speed, a roll angle speed, a yaw angle speed, an axial acceleration, a lateral acceleration, a vertical acceleration, a combined altitude, a BD altitude, a barometric altitude, a climb/dip rate, a vacuum speed, an indicated airspeed, a mach number, a BD speed, a combined north speed, a combined east speed, a combined airspeed, a BD north speed, a BD east speed, a BD airspeed, a left elevator given angle, a left elevator offset angle, a right elevator given angle, a right elevator offset angle, a left aileron given angle, a left aileron offset angle, a right aileron given angle, a left rudder offset angle, a right rudder given angle, a main bus voltage, a battery voltage, and the like.

A prediction module 202, configured to determine whether a fault exists in the aircraft according to the historical flight data and a fault prediction model, and if so, determine a fault type and a fault score of the aircraft, where the fault score is used to characterize a severity of the fault type predicted by the fault prediction model.

Wherein a fault score is used to characterize the severity of the fault type predicted by the fault prediction model. For example, the fault score may be 1, 2, 3, 4, 5 points, with higher scores being more severe for the fault type.

The fault type may be determined based on the degree of anomaly in the historical flight data, such as an anomaly in the pitch angle rate of the aircraft, such as an excessive or insufficient pitch angle rate, which may be due to a stuck pitch grid rudder, and thus the current fault type may be a stuck grid rudder.

Optionally, the prediction module may be further specifically configured to:

By way of example only, taking pitch angle rate anomalies as an example, assuming that the input received by the fault prediction model is the pitch angle rate of the aircraft at a certain time period, a value corresponding to the fault classification interval may be output, for example 50, and assuming that the fault classification interval corresponding to the stuck pitch grid rudder is 48-58, the fault type of the aircraft may be determined as the stuck pitch grid rudder.

It will be appreciated that the more serious the fault type is, the higher the fault score is, since the fault score is used to characterize the severity of the fault type as predicted by the fault prediction model.

In some embodiments, the scoring criteria for the fault score S may be expressed by the following formula:

wherein, the liquid crystal display device comprises a liquid crystal display device,

for the deviation degree score of the current historical flight data and the standard data of the same type, for example, the deviation degree score of the current pitch angle speed and the standard pitch angle speed, +.>

And the similarity score of the current historical flight data and the same type of larger abnormal data.

The fault degree determining module 203 is configured to determine a fault degree of the aircraft according to the fault score and fault data in a fault database, where the fault data includes at least one of a fault occurrence frequency, a fault weight, a fault duration, and a fault occurrence period.

obtaining the prediction accuracy of the fault prediction model;

；

wherein j is the failure mode,

for the prediction accuracy, S is the fault score, +.>

For the frequency of occurrence of said fault +.>

For the frequency of occurrence of said fault, +.>

And the failure degree of the failure mode j. The fault occurrence period T may be daytime, evening, night time, or the like.

Optionally, the prediction module is further specifically configured to:

In some embodiments, the initial fault prediction model may be a neural network model. In some embodiments, the neural network model may include a convolutional recurrent neural network (Convolutional Recurrent Neural Network, CRNN), a convolutional neural network (Convolutional neural networks, CNN), a deep convolutional neural network (Deep Convolutional Neural Networks, DCNN), a recurrent neural network (Recurrent neural networks, RNN), a Long/Short Term Memory, LSTM) model, and the like. In some embodiments, the initial model may adjust the internal parameters based on training conditions.

In some embodiments, the model may be optimized by constructing a loss function based on the prediction result of the initial failure prediction model and the sample true value, and adjusting the parameters in the model based on the gradient value of the loss function in the opposite direction. In some embodiments, during training, sample data in the validation set may be input into the trained model for calculation to obtain an output value (i.e., a validation result), and model parameters may be adjusted to optimize the model based on the validation result (e.g., the model is in an under-fit and/or over-fit state). The data in the verification set is independently co-distributed with the training data of the initial model and has no intersection. Comparing the verification result of the sample data with the identification of the corresponding sample data, and judging whether the training result meets the requirement. And if the training result does not meet the requirement, preparing the sample data again or dividing the training set and the verification set again, and continuing training. If the training result meets the requirement, model training can be stopped, and the finally trained fault prediction model is output as a required machine learning model.

In some embodiments, the training samples may be historical flight data, and the sample tags may be whether the historical flight data is anomalous, causes an aircraft failure, a corresponding failure type, a failure score, or the like.

And a maintenance module 204, configured to determine a maintenance strategy of the aircraft according to the failure degree, and send the maintenance strategy to a workbench.

Optionally, the maintenance module is further specifically configured to:

acquiring a maintenance strategy model;

In some embodiments, the maintenance strategy model may be similar to the failure prediction model and be a neural network model, and the maintenance strategy model may output a corresponding maintenance strategy through calculation of the neural network according to the failure data and the failure degree, for example, the maintenance strategy may be a maintenance grid rudder, an engine valve, and output information of suggested maintenance time, number of people, maintenance means, maintenance accessories, and the like.

In some embodiments, normalizing historical flight data may be expressed as the following:

where X represents the raw data, i.e. the raw flight data,

、/>

representing maximum and minimum values in the original data, < >>

Representing normalized data, ++>

After model training is completed, the predicted data is inversely normalized to obtain a normal predicted value, which can be expressed as the following formula:

it can be understood that the original data after normalization processing is more convenient for the fault prediction model to analyze and calculate, and the inverse normalized flight data can be effectively compared and analyzed with the actual value.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working processes of the modules/units/sub-units/components in the above-described apparatus may refer to corresponding processes in the foregoing method embodiments, which are not described herein again.

As can be seen from the above, when the depth estimation is performed on the image of the target scene, the embodiment of the application can take into consideration the losses of the camera for monocular image depth estimation and the target in multiple links such as relative pose, convolution processing, luminosity influence, sampling sequencing, and the like, and construct corresponding model units and loss functions, so that the prediction accuracy in each link is guaranteed to the greatest extent, and the total accuracy of monocular image depth estimation is improved.

Fig. 3 is a schematic flow chart of a prediction-based aircraft data analysis and maintenance system according to an embodiment of the present application, and as shown in fig. 3, the prediction-based aircraft data analysis and maintenance system includes the following steps:

step 301, historical flight data of a target aircraft are acquired.

Step 302, determining whether the aircraft has a fault according to the historical flight data and a fault prediction model, and if so, determining a fault type and a fault score of the aircraft, wherein the fault score is used for representing the severity degree of the fault type predicted by the fault prediction model;

step 303, determining the fault degree of the aircraft according to the fault scores and fault data in a fault database, wherein the fault data comprises at least one of fault occurrence frequency, fault influence probability, fault duration and fault occurrence period;

and 304, determining a maintenance strategy of the aircraft according to the fault degree, and sending the maintenance strategy to a workbench.

Therefore, according to the embodiment of the application, whether the aircraft has faults, fault types, fault scores and the like can be effectively predicted through historical flight data of the aircraft, the fault degree and maintenance strategy of the aircraft can be given after the data are obtained, and in addition, real-time data of the aircraft can be analyzed, so that the fault data and the maintenance strategy can be effectively combined.

It will be appreciated that in the specific embodiments of the present application, related data relating to user information, user characteristics, user health status, etc. are required to obtain user permissions or agreements when the above embodiments of the present application are applied to specific products or technologies, and the collection, use and processing of related data is required to comply with relevant laws and regulations and standards of relevant countries and regions.

In some embodiments, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing relevant data of the image acquisition device. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a predictive-based aircraft data analysis and maintenance system and system.

In some embodiments, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program, when executed by the processor, implements a predictive-based aircraft data analysis and repair system and system. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.

In some embodiments, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.

In some embodiments, a computer readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

In summary, the present application provides a prediction-based aircraft data analysis and repair system, comprising:

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments provided in the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.

Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present application, and are not intended to limit the scope of the present application, but the present application is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, the present application is not limited thereto. Any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the technical scope of the disclosure of the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the corresponding technical solutions. Are intended to be encompassed within the scope of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A predictive aircraft data analysis and repair system, comprising:

the maintenance module is used for determining a maintenance strategy of the aircraft according to the fault degree and sending the maintenance strategy to a workbench;

wherein, the failure degree determining module is further specifically configured to:

obtaining the prediction accuracy of the fault prediction model;

determining a fault degree of the aircraft according to the prediction accuracy, the fault score and fault data in a fault database, wherein the fault degree is expressed as:

；

wherein j is a fault mode,

for the prediction accuracy, S is the fault score, +.>

For the frequency of occurrence of said fault +.>

For the frequency of occurrence of said fault, +.>

And the failure degree of the failure mode j.

2. The prediction-based aircraft data analysis and repair system of claim 1, wherein the prediction module is further specifically configured to:

determining a fault classification value corresponding to the historical flight data according to the historical flight data;

3. The prediction-based aircraft data analysis and repair system of claim 2, wherein the prediction module is further specifically configured to: and determining the fault score according to the fault type and the historical flight data corresponding to the fault type.

4. The prediction-based aircraft data analysis and repair system of claim 1, wherein the prediction module is further specifically configured to:

5. The prediction-based aircraft data analysis and repair system of claim 1, wherein the repair module is further specifically configured to:

acquiring a maintenance strategy model;

6. The prediction-based aircraft data analysis and repair system of claim 5, wherein the acquisition module is further specifically configured to: and normalizing and de-normalizing the historical flight data.

7. A method of analyzing and maintaining a system based on predicted aircraft data according to any one of claims 1-6, the method comprising:

acquiring historical flight data of a target aircraft;

determining a maintenance strategy of the aircraft according to the fault degree, and sending the maintenance strategy to a workbench;

wherein determining the degree of failure of the aircraft based on the failure score and the failure data in the failure database comprises:

；

wherein j is a fault mode,

for the prediction accuracy, S is the fault score, +.>

For the frequency of occurrence of said fault +.>

For the frequency of occurrence of said fault, +.>

And the failure degree of the failure mode j.

8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed, implements the steps of the method according to claim 7.

9. A computer device comprising a processor, a memory and a computer program stored on the memory, characterized in that the processor implements the steps of the method according to claim 7 when executing the computer program.